scholarly journals Benefits and challenges of Big Data in healthcare: an overview of the European initiatives

2019 ◽  
Vol 29 (Supplement_3) ◽  
pp. 23-27 ◽  
Author(s):  
Roberta Pastorino ◽  
Corrado De Vito ◽  
Giuseppe Migliara ◽  
Katrin Glocker ◽  
Ilona Binenbaum ◽  
...  
Keyword(s):  
Public Health ◽  
Big Data ◽  
Best Practice ◽  
Clinical Care ◽  
Healthcare Providers ◽  
Big Data Analytics ◽  
Well Being ◽  
Ageing Population ◽  
Healthcare Organizations ◽  
Healthcare Data

Abstract Healthcare systems around the world are facing incredible challenges due to the ageing population and the related disability, and the increasing use of technologies and citizen’s expectations. Improving health outcomes while containing costs acts as a stumbling block. In this context, Big Data can help healthcare providers meet these goals in unprecedented ways. The potential of Big Data in healthcare relies on the ability to detect patterns and to turn high volumes of data into actionable knowledge for precision medicine and decision makers. In several contexts, the use of Big Data in healthcare is already offering solutions for the improvement of patient care and the generation of value in healthcare organizations. This approach requires, however, that all the relevant stakeholders collaborate and adapt the design and performance of their systems. They must build the technological infrastructure to house and converge the massive volume of healthcare data, and to invest in the human capital to guide citizens into this new frontier of human health and well-being. The present work reports an overview of best practice initiatives in Europe related to Big Data analytics in public health and oncology sectors, aimed to generate new knowledge, improve clinical care and streamline public health surveillance.

Big Data and Machine Learning

2018 ◽  
pp. 225-239
Author(s):  
Fernando Enrique Lopez Martinez ◽  
Edward Rolando Núñez-Valdez
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Complete Solution ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Healthcare Organizations ◽  
High Level

IoT, big data, and artificial intelligence are currently three of the most relevant and trending pieces for innovation and predictive analysis in healthcare. Many healthcare organizations are already working on developing their own home-centric data collection networks and intelligent big data analytics systems based on machine-learning principles. The benefit of using IoT, big data, and artificial intelligence for community and population health is better health outcomes for the population and communities. The new generation of machine-learning algorithms can use large standardized data sets generated in healthcare to improve the effectiveness of public health interventions. A lot of these data come from sensors, devices, electronic health records (EHR), data generated by public health nurses, mobile data, social media, and the internet. This chapter shows a high-level implementation of a complete solution of IoT, big data, and machine learning implemented in the city of Cartagena, Colombia for hypertensive patients by using an eHealth sensor and Amazon Web Services components.

Towards a Better and Smarter Healthcare and IoT

2019 ◽  
pp. 191-228
Author(s):  
Navin Kumar
Keyword(s):  
Big Data ◽  
Quality Of Care ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Systems ◽  
Healthcare Organizations ◽  
Healthcare Data ◽  
Value Based Care ◽  
Rich Data

The amount of healthcare data continues to exponentially grow everyday. The complexity of this data further limits the analytical capabilities of traditional healthcare systems. With value-based care, it is far more imminent for healthcare organizations to control the costs and to improve the quality of care in order to sustain their business. The purpose of the chapter is to gain insights into complexities and challenges that exist in current healthcare systems and how big data analytics and IoT can play a pivotal role to positively influence the quality of care and patient outcomes. The chapter also provides solutions and strategies for building cloud-based data asset that can deliver rich data analytics to both the healthcare systems and the patients.

Importance of Big Data In Healthcare System A Survey Approach

2020 ◽  
pp. 47-56
Author(s):  
M. Pavithra ◽  
E. S. Shamila ◽  
G. Krishna Priya ◽  
G. VijiPriya ◽  
R. Ashwini
Keyword(s):  
Big Data ◽  
Healthcare Providers ◽  
Public Healthcare ◽  
Healthcare Industry ◽  
Healthcare Organizations ◽  
Public And Private ◽  
Healthcare Data ◽  
System A ◽  
Efficient Management ◽  
Public And Private Sector

<p>‘Big data’ is massive amounts of information that can work wonders. It has become a topic of special interest for the past two decades because of a great potential that is hidden in it. Various public and private sector industries generate, store, and analyze big data with an aim to improve the services they provide. In the healthcare industry, various sources for big data include hospital records, medical records of patients, and results of medical examinations, and devices that are a part of internet of things. Biomedical research also generates a significant portion of big data relevant to public healthcare. This data requires proper management and analysis in order to derive meaningful information. Otherwise, seeking solution by analyzing big data quickly becomes comparable to finding a needle in the haystack. There are various challenges associated with each step of handling big data which can only be surpassed by using high-end computing solutions for big data analysis. That is why, to provide relevant solutions for improving public health, healthcare providers are required to be fully equipped with appropriate infrastructure to systematically generate and analyze big data. An efficient management, analysis, and interpretation of big data can change the game by opening new avenues for modern healthcare. That is exactly why various industries, including the healthcare industry, are taking vigorous steps to convert this potential into better services and financial advantages. With a strong integration of biomedical and healthcare data, modern healthcare organizations can possibly revolutionize the medical therapies and personalized medicine.</p>

scholarly journals Big Healthcare Data: Survey of Challenges and Privacy

2020 ◽  
Vol 4 (4) ◽  
pp. 184
Author(s):  
Mohammed Bin Jubeir ◽  
Mohd Arfian Ismail ◽  
Shahreen Kasim ◽  
Hidra Amnur ◽  
Defni
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Healthcare Delivery ◽  
Big Data Analytics ◽  
Quality Of Healthcare ◽  
Care Providers ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Wide Range

The last century witnessed a dramatic leap in the shift towards digitizing the healthcare workflow and moving to e-patients' records. Health information is consistently becoming more diverse and complex, leading to the so-called massive data. Additionally, the demand for big data analytics in healthcare organizations is increasingly growing with the aim of providing a wide range of unprecedented potentials that are considered necessary for the provision of meaningful information about big data and improve the quality of healthcare delivery. It also aims to increase the effectiveness and efficiency of healthcare organizations; provide doctors and care providers better decision-making information and help them in the early detection of diseases. It also assists in evidence-based medicine and helps to minimize healthcare cost. However, a clear contradiction exists between the privacy and security of big data and its widespread usage. In this paper, the focus is on big data with respect to its characteristics, trends, and challenges. Additionally, the risks and benefits associated with data analytics were reviewed.

Survey of Cloud Computing in Healthcare Sector

2017 ◽  
Vol 7 (7) ◽  
pp. 337
Author(s):  
S. Karthiga Devi ◽  
B. Arputhamary
Keyword(s):  
Cloud Computing ◽  
Healthcare Providers ◽  
High Volume ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Cloud Infrastructure ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Number Of Patients

Today the volume of healthcare data generated increased rapidly because of the number of patients in each hospital increasing.  These data are most important for decision making and delivering the best care for patients. Healthcare providers are now faced with collecting, managing, storing and securing huge amounts of sensitive protected health information. As a result, an increasing number of healthcare organizations are turning to cloud based services. Cloud computing offers a viable, secure alternative to premise based healthcare solutions. The infrastructure of Cloud is characterized by a high volume storage and a high throughput. The privacy and security are the two most important concerns in cloud-based healthcare services. Healthcare organization should have electronic medical records in order to use the cloud infrastructure. This paper surveys the challenges of cloud in healthcare and benefits of cloud techniques in health care industries.

scholarly journals Big Data Analytics in urology: the story so far and the road ahead

2021 ◽  
Vol 13 ◽  
pp. 175628722199813
Author(s):  
B. M. Zeeshan Hameed ◽  
Aiswarya V. L. S. Dhavileswarapu ◽  
Nithesh Naik ◽  
Hadis Karimi ◽  
Padmaraj Hegde ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Early Stage ◽  
Big Data Analytics ◽  
Positive Trend ◽  
The Road ◽  
Healthcare Data ◽  
Urological Disorders ◽  
Different Sources

Artificial intelligence (AI) has a proven record of application in the field of medicine and is used in various urological conditions such as oncology, urolithiasis, paediatric urology, urogynaecology, infertility and reconstruction. Data is the driving force of AI and the past decades have undoubtedly witnessed an upsurge in healthcare data. Urology is a specialty that has always been at the forefront of innovation and research and has rapidly embraced technologies to improve patient outcomes and experience. Advancements made in Big Data Analytics raised the expectations about the future of urology. This review aims to investigate the role of big data and its blend with AI for trends and use in urology. We explore the different sources of big data in urology and explicate their current and future applications. A positive trend has been exhibited by the advent and implementation of AI in urology with data available from several databases. The extensive use of big data for the diagnosis and treatment of urological disorders is still in its early stage and under validation. In future however, big data will no doubt play a major role in the management of urological conditions.

scholarly journals Predictors of outpatients’ no-show: big data analytics using apache spark

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tahani Daghistani ◽  
Huda AlGhamdi ◽  
Riyad Alshammari ◽  
Raed H. AlHazme
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Negative Impact ◽  
Big Data Analytics ◽  
Quality Of Healthcare ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Healthcare Organizations ◽  
Data Framework ◽  
Learning Techniques

AbstractOutpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in the Spark platform. This study evaluates the performance of five machine learning techniques, using the (2,011,813‬) outpatients’ visits data. Conducting several experiments and using different validation methods, the Gradient Boosting (GB) performed best, resulting in an increase of accuracy and ROC to 79% and 81%, respectively. In addition, we showed that exploring and evaluating the performance of the machine learning models using various evaluation methods is critical as the accuracy of prediction can significantly differ. The aim of this paper is exploring factors that affect no-show rate and can be used to formulate predictions using big data machine learning techniques.

Big Data and Big Data Analytics for Improved Healthcare Service and Management

2020 ◽  
Vol 8 (1) ◽  
pp. 13-51
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Moutan Mukhopadhyay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Future Market ◽  
Data Generation ◽  
Related Data ◽  
Healthcare Data ◽  
Different Types

Like other fields, the healthcare sector has also been greatly impacted by big data. A huge volume of healthcare data and other related data are being continually generated from diverse sources. Tapping and analysing these data, suitably, would open up new avenues and opportunities for healthcare services. In view of that, this paper aims to present a systematic overview of big data and big data analytics, applicable to modern-day healthcare. Acknowledging the massive upsurge in healthcare data generation, various ‘V's, specific to healthcare big data, are identified. Different types of data analytics, applicable to healthcare, are discussed. Along with presenting the technological backbone of healthcare big data and analytics, the advantages and challenges of healthcare big data are meticulously explained. A brief report on the present and future market of healthcare big data and analytics is also presented. Besides, several applications and use cases are discussed with sufficient details.

scholarly journals The Benefits of Big Data Analytics in the Healthcare Sector

2015 ◽  
pp. 406-439 ◽  
Author(s):  
Andrea Darrel ◽  
Margee Hume ◽  
Timothy Hardie ◽  
Jeffery Soar
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Healthcare Providers ◽  
Big Data Analytics ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Healthcare Sector ◽  
Improved Outcomes ◽  
Health Initiatives ◽  
Remote Patient

The benefits of big data analytics in the healthcare sector are assumed to be substantial, and early proponents have been very enthusiastic (Chen, Chiang, & Storey, 2012), but little research has been carried out to confirm just what those benefits are, and to whom they accrue (Bollier, 2010). This chapter presents an overview of existing literature that demonstrates quantifiable, measurable benefits of big data analytics, confirmed by researchers across a variety of healthcare disciplines. The chapter examines aspects of clinical operations in healthcare including Cost Effectiveness Research (CER), Clinical Decision Support Systems (CDS), Remote Patient Monitoring (RPM), Personalized Medicine (PM), as well as several public health initiatives. This examination is in the context of searching for the benefits described resulting from the deployment of big data analytics. Results indicate the principle benefits are delivered in terms of improved outcomes for patients and lower costs for healthcare providers.

Discovering User Preferences for Digital Health in Well-being Apps (Preprint)

2021 ◽  
Author(s):  
Neta Kela ◽  
Eleanor Eytam ◽  
Adi Katz
Keyword(s):  
Public Health ◽  
Digital Health ◽  
New Technology ◽  
Well Being ◽  
Major Product ◽  
User Preferences ◽  
Healthcare Organizations ◽  
Wide Range ◽  
Range Of Functions ◽  
Mhealth Apps

UNSTRUCTURED The desire for healthcare organizations to reduce the cost of chronic care and to prevent disease from occurring to begin with, has coincided with the development of new technology that is revolutionizing digital health. Numerous health-oriented mobile phone applications (referred to as mHealth apps) have been developed and are available for download into smartphones. These mHealth apps serve a wide range of functions. There are apps that monitor data to treat or avoid chronic illness; apps for managing daily activities and diet; apps promoting healthy choices for people who want to maintain and improve their overall health, and many others. While it is generally recognized that mHealth apps have a significant potential for promoting public health, little research has been done to determine user preferences for such apps. Understanding what users want in their mHealth apps can help increase their acceptability and encourage healthy lifestyles. The research in this article tests the major product qualities of such apps, asking two key questions: Do users seek interaction with a live physician, or are they willing to rely on artificial intelligence to analyze data from their app? Which aspects of their app do they consider as having a positive instrumental, aesthetic, or symbolic value? Next, the research presented here tests how these judgments influence product preference. The contribution of this paper is its focus on user preferences which may help in the design of mHealth apps to better address peoples’ needs—thus encouraging a wide, frequent, and effective use of such tools which promote public health.

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